8 Strategies for Assessing Residual Independence in Linear Regression

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The assumption of independence of residuals refers to the requirement that the residuals (the differences between the observed values and the values predicted by the model) are independent of each other. This assumption is crucial for the validity of statistical inferences and the reliability of the regression model.

• Independence of residuals means that the error terms (residuals) for different observations are not correlated with each other. In other words, the occurrence of a particular residual does not provide information about the likely occurrence or magnitude of another residual.
• The assumption of independence is essential for making unbiased and efficient estimates of the regression coefficients. If residuals are correlated, it can lead to inefficiency in coefficient estimates, affecting the precision of hypothesis tests and confidence intervals.

Assessing the independence of residuals in a linear regression model is crucial to ensure the validity of statistical inferences. Here are several methods to assess the independence of residuals:

1. Residual Plot

• Method: Plot the residuals against the predicted values or against the independent variable(s).
• Interpretation: Look for any…